Client Accounts
What Organisations Say After Working With Us
Honest accounts from clients who have engaged Axiologic for data audits, recommendation systems, and AI organisational design.
Back to Home47+
Organisations Served
4.8
Average Satisfaction Score
94%
On-Time Delivery Rate
82%
Clients Return for Second Engagement
Reviews
From the Organisations We Have Worked With
Wei Ling Tan
Head of Data, Logistics Platform · Singapore
The data audit surfaced problems we had suspected for years but never formally documented. The report was detailed enough to actually act on — not a vague set of recommendations. Three months later, we are well into the remediation work and seeing measurable improvements in our pipeline reliability.
February 2026 · Data Quality Audit
Arjun Mehta
VP of Engineering, E-commerce Platform · Singapore
We have worked with AI vendors before who handed over a black box and left. Axiologic was different — they explained every design decision, built in the A/B testing setup from day one, and made sure our product team understood how to adjust the recommendation parameters without needing to call them. That independence was the whole point.
January 2026 · Recommendation Engine
Sarah Lim
Chief Operating Officer, Financial Services Firm · Singapore
The AI CoE playbook was genuinely useful — not the kind of generic framework you can find in any consulting library. The role definitions were written for our specific team structure, and the pilot project plan gave our leadership something concrete to approve. The governance templates alone saved us weeks of internal drafting.
January 2026 · AI CoE Design
Raj Kumar
CTO, SaaS Platform · Singapore
We used the recommendation engine for our content platform and were pleased with both the quality of the work and how well it held up after deployment. There were some edge cases during the validation period that the team handled quickly. We now run regular A/B tests using the framework they set up — something we would not have done without it in place.
February 2026 · Recommendation Engine
Li Hua Chen
Director of Strategy, Regional Enterprise · Singapore
The data audit was straightforward to initiate — we signed a scope document, had a kickoff session, and the work proceeded with minimal internal burden. The findings report was honest about what would require significant effort to fix, which we appreciated. We needed an independent view, and that is what we got.
February 2026 · Data Quality Audit
David Gopinath
Head of Product, Media Platform · Singapore
We came to Axiologic for the CoE design after spending two years with scattered AI experiments that were not connecting. The engagement helped us build a proper vocabulary for AI governance internally — which sounds minor but was actually a significant blocker before. The pilot project plan gave us a first credible AI initiative to rally the organisation around.
January 2026 · AI CoE Design
Case Studies
Three Engagements in Detail
E-commerce Platform · Recommendation Engine · Singapore
6 weeks · SGD 1,650Challenge
A Singapore-based e-commerce platform with 85,000 active users had no personalisation in place. Product discovery was driven entirely by category browsing. Average session depth and conversion rates had plateaued despite catalogue growth.
Approach
Axiologic analysed 18 months of interaction data and developed a hybrid recommendation model combining collaborative filtering with content-based signals. An A/B testing framework was built alongside the system to allow progressive rollout and ongoing measurement.
Results
At the six-week mark, the client had a deployed recommendation layer, an operational A/B framework, and a product team confident in adjusting recommendation parameters. Session depth improved by approximately 23% in the first validated A/B test post-deployment.
Logistics Operator · Data Quality Audit · Singapore
3 weeks · SGD 420Challenge
A logistics operator with five years of route and shipment data wanted to build a predictive demand model. Internal estimates of data quality were optimistic. The technical team was uncertain whether to proceed without an independent assessment.
Approach
Axiologic reviewed seven key datasets covering shipment records, route logs, and demand signals. Interviews were conducted with data owners in operations and IT. A formal quality scoring methodology was applied across completeness, consistency, and structural integrity dimensions.
Results
The audit identified two dataset categories that were materially unsuitable for model training due to labeling inconsistencies, and four areas that required moderate cleaning before use. The client used the report to scope a four-month data remediation programme with clear success criteria.
Financial Services Firm · AI CoE Design · Singapore
5 weeks · SGD 2,380Challenge
A regional financial services firm had run three separate AI pilot projects across different business units over two years. The pilots were technically reasonable but unconnected. Leadership wanted to build a coordinated AI capability without creating a large standalone team.
Approach
Axiologic interviewed stakeholders across three business units and technology leadership to map existing AI activity, toolchains, and data access structures. An organisational model was developed for a lean CoE structure with cross-functional participation and clear governance over project prioritisation.
Results
The engagement delivered a full playbook including team structure, role definitions, governance templates, a toolchain recommendation, and a first pilot project plan that leadership approved within three weeks of delivery. The CoE was formally established within two months of engagement close.
Contact Axiologic
Reach Us Directly
Start an Engagement
Ready to Talk About Your AI Situation?
An initial conversation with Axiologic costs nothing. If there is a fit, we will propose a scoped engagement. If there is not, we will say so.
Get in Touch